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Diffstat (limited to 'tensorflow/examples/image_retraining/retrain_test.py')
| -rw-r--r-- | tensorflow/examples/image_retraining/retrain_test.py | 148 |
1 files changed, 0 insertions, 148 deletions
diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py deleted file mode 100644 index fb7324c58a..0000000000 --- a/tensorflow/examples/image_retraining/retrain_test.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -# pylint: disable=g-bad-import-order,unused-import -"""Tests the graph freezing tool.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf -import os - -from tensorflow.examples.image_retraining import retrain -from tensorflow.python.framework import test_util - - -class ImageRetrainingTest(test_util.TensorFlowTestCase): - - def dummyImageLists(self): - return {'label_one': {'dir': 'somedir', 'training': ['image_one.jpg', - 'image_two.jpg'], - 'testing': ['image_three.jpg', 'image_four.jpg'], - 'validation': ['image_five.jpg', 'image_six.jpg']}, - 'label_two': {'dir': 'otherdir', 'training': ['image_one.jpg', - 'image_two.jpg'], - 'testing': ['image_three.jpg', 'image_four.jpg'], - 'validation': ['image_five.jpg', 'image_six.jpg']}} - - def testGetImagePath(self): - image_lists = self.dummyImageLists() - self.assertEqual('image_dir/somedir/image_one.jpg', retrain.get_image_path( - image_lists, 'label_one', 0, 'image_dir', 'training')) - self.assertEqual('image_dir/otherdir/image_four.jpg', - retrain.get_image_path(image_lists, 'label_two', 1, - 'image_dir', 'testing')) - - def testGetBottleneckPath(self): - image_lists = self.dummyImageLists() - self.assertEqual('bottleneck_dir/somedir/image_five.jpg_imagenet_v3.txt', - retrain.get_bottleneck_path( - image_lists, 'label_one', 0, 'bottleneck_dir', - 'validation', 'imagenet_v3')) - - def testShouldDistortImage(self): - self.assertEqual(False, retrain.should_distort_images(False, 0, 0, 0)) - self.assertEqual(True, retrain.should_distort_images(True, 0, 0, 0)) - self.assertEqual(True, retrain.should_distort_images(False, 10, 0, 0)) - self.assertEqual(True, retrain.should_distort_images(False, 0, 1, 0)) - self.assertEqual(True, retrain.should_distort_images(False, 0, 0, 50)) - - def testAddInputDistortions(self): - with tf.Graph().as_default(): - with tf.Session() as sess: - retrain.add_input_distortions(True, 10, 10, 10, 299, 299, 3, 128, 128) - self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortJPGInput:0')) - self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortResult:0')) - - @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01) - def testAddFinalRetrainOps(self, flags_mock): - with tf.Graph().as_default(): - with tf.Session() as sess: - bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') - # Test creating final training op with quantization. - retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, False, - False) - self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) - - @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01) - def testAddFinalRetrainOpsQuantized(self, flags_mock): - # Ensure that the training and eval graph for quantized models are correctly - # created. - with tf.Graph().as_default() as g: - with tf.Session() as sess: - bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') - # Test creating final training op with quantization, set is_training to - # true. - retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, True) - self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) - found_fake_quant = 0 - for op in g.get_operations(): - if op.type == 'FakeQuantWithMinMaxVars': - found_fake_quant += 1 - # Ensure that the inputs of each FakeQuant operations has 2 Assign - # operations in the training graph (Assign[Min,Max]Last, - # Assign[Min,Max]Ema) - self.assertEqual(2, - len([i for i in op.inputs if 'Assign' in i.name])) - self.assertEqual(found_fake_quant, 2) - with tf.Graph().as_default() as g: - with tf.Session() as sess: - bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') - # Test creating final training op with quantization, set is_training to - # false. - retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, False) - self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) - found_fake_quant = 0 - for op in g.get_operations(): - if op.type == 'FakeQuantWithMinMaxVars': - found_fake_quant += 1 - for i in op.inputs: - # Ensure that no operations are Assign operation since this is the - # evaluation graph. - self.assertTrue('Assign' not in i.name) - self.assertEqual(found_fake_quant, 2) - - def testAddEvaluationStep(self): - with tf.Graph().as_default(): - final = tf.placeholder(tf.float32, [1], name='final') - gt = tf.placeholder(tf.int64, [1], name='gt') - self.assertIsNotNone(retrain.add_evaluation_step(final, gt)) - - def testAddJpegDecoding(self): - with tf.Graph().as_default(): - jpeg_data, mul_image = retrain.add_jpeg_decoding(10, 10, 3, 0, 255) - self.assertIsNotNone(jpeg_data) - self.assertIsNotNone(mul_image) - - def testCreateModelInfo(self): - did_raise_value_error = False - try: - retrain.create_model_info('no_such_model_name') - except ValueError: - did_raise_value_error = True - self.assertTrue(did_raise_value_error) - model_info = retrain.create_model_info('inception_v3') - self.assertIsNotNone(model_info) - self.assertEqual(299, model_info['input_width']) - - def testCreateModelInfoQuantized(self): - # Test for mobilenet_quantized - model_info = retrain.create_model_info('mobilenet_1.0_224') - self.assertIsNotNone(model_info) - self.assertEqual(224, model_info['input_width']) - - -if __name__ == '__main__': - tf.test.main() |